Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy. In-flow phase-contrast tomography provides 3D refractive index tomograms of suspended cells in label-free mode. Fluorescence readout is avoided, thus the method is not organelle-specific. Generalized computational segmentation based on the statistical inference adds specificity to the method, identifying intracellular compartments. Fruition of tomograms through Virtual Reality is introduced for the first time. Users access quantitative data of each organelle on-demand, inspecting cells from outside or inside.image

Automated summary

A generalized computational strategy based on self-consistent statistical inference is shown to achieve intracellular multi-specificity in single cells. Additionally, virtual reality is introduced for the first time to handle the information content of multi-specificity in single cells. This opens the way to a metaverse for 3D microscopy.